Djulbegovic B.,Center for Evidence Based Medicine and Health Outcomes Research | Djulbegovic B.,University of South Florida | Djulbegovic B.,H. Lee Moffitt Cancer Center and Research Institute | Kumar A.,Center for Evidence Based Medicine and Health Outcomes Research | And 16 more authors.PLoS ONE | Year: 2013

WHENEVER A TRIAL IS CONDUCTED, THERE ARE THREE POSSIBLE EXPLANATIONS FOR THE RESULTS: a) findings are correct (truth), b) represents random variation (chance) or c) they are influenced by systematic error (bias). Random error is deviation from the truth and happens due to play of chance (e.g. trials with small sample, etc.). Systematic distortion of the estimated intervention effect away from the truth can also be caused by inadequacies in the design, conduct or analysis of a trial. Several studies have shown that bias can obscure up to 60% of the real effect of a healthcare intervention. A mounting body of empirical evidence shows that biased results from poorly designed and reported trials can mislead decision making in healthcare at all levels. Poorly conducted and reported RCTs seriously compromise the integrity of the research process especially when biased results receive false credibility. Therefore, critical appraisal of the quality of clinical research is central to informed decision-making in healthcare. Critical appraisal is the process of carefully and systematically examining research evidence to judge its trustworthiness, its value and relevance in a particular context. It allows clinicians to use research evidence reliably and efficiently. Critical appraisal is intended to enhance the healthcare professionals skill to determine whether the research evidence is true (free of bias) and relevant to their patients.

Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease.We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice.We show that physicians beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision makers threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice.We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the large extent dominated by expected utility theory. The model also provides a platform for reconciling two groups of competing dual processing theories (parallel competitive with default-interventionalist theories).